Welcome!

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We are interested in creating new materials and biomaterials promising for use in a number of technologically important areas, such as energy, biomedicine, and tribology. We apply statistical mechanical theory and multi-scale computational methods, combined with machine learning and optimization algorithms, to improve fundamental understanding of the structure-property relationships in the existing materials. Specifically, we develop new transferable all-atom and coarse-grained models of the existing materials to investigate their reactive, structural, and dynamical properties. To explore the effects of change in the surrounding environment, on structural and dynamical properties of these materials, they are exposed to the distinctive experimental environment on the computer that is created by applying novel strategies and approaches. The meso-scale nature of these models allows the direct comparison with experiments and improves our understanding towards the existing materials. A deeper understanding of the molecular-level structure and dynamics of the existing materials and use of machine learning approaches empower us to design new hybrid materials with predefined structure and function that can be used in next generation devices.